Triple
T14067128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Voiron |
E338503
|
entity |
| Predicate | distanceToGrenoble_km |
P93638
|
FINISHED |
| Object | about 25 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: about 25 | Statement: [Voiron, distanceToGrenoble_km, about 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGrenoble_km Context triple: [Voiron, distanceToGrenoble_km, about 25]
-
A.
distanceToGrenobleKilometers
chosen
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Grenoble.
-
B.
distanceToChambéryKilometersApprox
Indicates an approximate distance, measured in kilometers, between a given entity and the location of Chambéry.
-
C.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
D.
distanceToClermontFerrand_km
Indicates the physical distance, measured in kilometers, between a given place and Clermont-Ferrand.
-
E.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de568b81f08190a571004261c0e8e4 |
completed | April 14, 2026, 3 p.m. |
| PD | Predicate disambiguation | batch_69de05adef888190b023ab42ef5076b6 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:21 p.m.